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❇️Mastering Optimization with Slime Mould Algorithm: A MATLAB Tutorial
Dive into our MATLAB tutorial on the Slime Mould Algorithm (SMA) for stochastic optimization. Learn how SMA, inspired by nature, addresses complex optimization problems. This video covers SMA's basics, its MATLAB implementation, and showcases its effectiveness with visualizations and examples, catering to both beginners and experts. Ideal for researchers, students, and enthusiasts in computational intelligence, this tutorial is designed to enrich your optimization knowledge and spark innovation.
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Discover the power of genetic programming in creating automated solutions for various problems such as controllers, antennas, genetic networks, and analog electrical circuits. The Genetic Programming IV book and video show how this approach can deliver high-return, human-competitive machine intelligence, and even create patentable inventions. With increasing computer time, results have progressively improved over 15 years. The video highlights the creation of a PID controller using genetic programming, emphasizing that results are human-competitive if they meet specific arm's length criteria.
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John Koza from Stanford University discuss genetic programming, which automatically creates programs from problem statements. Results produced are competitive with human-produced ones and even infringe on previously patented inventions. Genetic programming is an extension of the genetic algorithm and starts with randomly generated programs that undergo fitness evaluation, selection, and genetic operations. The resulting programs solve a variety of problems, reuse steps, and produce non-trivial results.
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This video has Persian subtitles and noise removal and quality enhancement.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_3

genetic programming, a method for computers to solve problems without explicit programming. Breeding randomly generated programs of different sizes and shapes, the fittest ones are selected for further breeding, creating better solutions over many generations. Stanford professor John Koza's research focuses on exploiting regularities and symmetries of complex environments for hierarchical organization and reuse. The ultimate goal is to enable computers to learn to solve non-trivial problems.
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This video has Persian subtitles and noise removal and quality enhancement.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_2

John Koza, a Stanford University researcher, developed genetic programming as a method to evolve computer programs by simulating the natural selection process. In this approach, a population of computer programs, composed of primitive functions and terminals, is evolved to solve a given problem. Each program's fitness is determined by its effectiveness in solving the problem. A few programs with high fitness are selected for reproduction, while many participate in a recombination operation called crossover. By iterating this process over multiple generations, the structure of a computer program that effectively solves the problem can emerge.
Download from Iranian server (VPN and proxy must be disconnected)
This video has Persian subtitles and noise removal and quality enhancement.
🆔 @MATLAB_House

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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #Part_1